Leslie Ann Owens Senior Lecturer, Information Technology Executive Director, Center for Information Systems Research (CISR) Nick van der Meulen Research Scientist, Sloan Center for Information Systems Research Clena Abuan Senior Technology Associate, BP Jerry Gupta P&C Research Lead, Swiss Re Institute
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Brian W Anthony Principal Research Scientist, Department of Mechanical Engineering Associate Director, MIT.nano Director of Technical Operations, Center for Clinical and Translational Research
Industry is undergoing a major transformation, shifting from automated to autonomous operations. The key to making this happen is the integration of digital technologies, including sensors, data, computing power, and information systems. At the heart of this shift are digital twins—virtual models that represent materials, processes, supply chains, and production lines. These digital replicas allow for simulation, monitoring, and improvement of operations in real-time using sensor data. When digital twins are combined with real-time control systems and machine learning, operations and factories become smarter and more adaptive. Real-time data flows from sensors to digital models and ML algorithms, enabling predictive maintenance, waste reduction, and optimizing production. A data-in-context connected ecosystem creates a highly efficient, data-driven environment in manufacturing and in mining.
William Fischer Senior Lecturer, Sloan School of Management
Generative models can now produce realistic and diverse synthetic data in many domains. This makes them a viable choice as a data source for training downstream AI systems. Unlike real data, synthetic data can be steered and optimized via interventions in the generative process. I will share my view on how this makes synthetic data act like data++, data with additional capabilities. I will discuss the advantages and disadvantages of this setting, and show several applications toward problems in computer vision and robotics.